neurodb 0.0.1

A smart and efficient database orchestration layer that simplifies multi-database interactions with intuitive operations and seamless Pandas integration.

Neuro-DB: Simplifying Multi-Database Interactions in Python

Neuro-DB is a powerful orchestration layer that bridges the gap between developers and databases. It provides an intuitive, unified interface to manage multiple database connections effortlessly, with seamless Pandas integration for querying, inserting, and updating records across multiple database types, including PostgreSQL, MySQL, SQL Server, and SQLite.

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Key Features

• 🔗 Multi-Database Connectivity – Supports PostgreSQL, MySQL, SQL Server, and SQLite.
• ⚡ Simplified Operations – Intuitive methods like get_df, upsert_df, and execute_query.
• 🐼 Seamless Pandas Integration – Read from and write directly to databases using Pandas DataFrames.
• 🔒 Secure Connection Management – Environment-based credential handling for enhanced security.
• 📈 Logging & Performance Tracking – Monitor and optimize query execution.
• ⚙️ Async and Sync Support – Handle operations efficiently with async execution.
• 🛠 Minimal Setup Required – Easy-to-use interface with powerful functionality.

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Installation

Install Neuro-DB via pip:

pip install neuro-db

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Quick Start

1. Configure your databases

from neuro_db import DatabaseManager

db_configs = {
    "postgres_main": {
        "dialect": "postgresql",
        "user": "admin",
        "password": "securepass",
        "host": "localhost",
        "port": 5432,
        "database": "company_db"
    }
}

db = DatabaseManager(db_configs)

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2. Perform database operations

Fetch data as a Pandas DataFrame

df = db.get_df("postgres_main", "SELECT * FROM employees WHERE department = %s", params=("HR",))
print(df.head())

Upsert data from a Pandas DataFrame

import pandas as pd

data = pd.DataFrame([
    {"id": 1, "name": "Alice", "department": "HR"},
    {"id": 2, "name": "Bob", "department": "IT"}
])

db.upsert_df("postgres_main", "employees", data, unique_key="id")

Write DataFrame directly to a table

df.to_sql("employees_backup", db.get_connection("postgres_main"), if_exists="replace", index=False)

Execute raw SQL queries

result = db.execute_query("postgres_main", "UPDATE employees SET status = %s WHERE id = %s", params=("active", 1))

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Supported Databases

Neuro-DB supports the following databases out of the box:

• PostgreSQL (via psycopg2)
• MySQL/MariaDB (via pymysql)
• SQL Server (via pyodbc)
• SQLite (built-in Python module)

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Configuration Options

You can configure Neuro-DB via environment variables or a YAML/JSON configuration file.
Example YAML config:

databases:
  mysql_db:
    dialect: mysql
    user: root
    password: password123
    host: localhost
    port: 3306
    database: sales_db

  sqlite_db:
    dialect: sqlite
    database: my_local.db

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Why Choose Neuro-DB?

• Unified API Across Databases – Work consistently across different database systems.
• Boost Productivity – Focus on building applications without database complexity.
• Optimized Performance – Built-in query optimizations and best practices.
• Production-Ready – With logging, retries, and error handling baked in.

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Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a feature branch.
3. Submit a pull request.

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License

This project is licensed under the Apache 2.0 License – free to use with attribution.

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